05276800

This is the program to build image pyramid and then we can decompose it in to the different layers.

精通MATLAB数字图像处理与识别 chapter4 代码(Proficient in MATLAB digital image processing and recognition chapter4 code)

基于神经网络对数字调制信号2ASK、2FSK、2PSK、4ASK、4FSK、4PSK进行门限值的判定来识别这六种数字调制信号。(Based on neural network digital modulation signal 2ASK, 2FSK, 2PSK, 4ASK, 4FSK, 4PSK threshold determination performed to identify the six digital modulation signal.)

仿真信号处理。。实现维纳滤波。。用MATLAB实现，。。。(Simulation of signal processing. . Wiener filter to achieve. . Using MATLAB realization. . .)

performs edge detection well

基于支持向量机(SVM)的住房需求模型预测的MATLAB代码。(Based on support vector machine (SVM) model to predict the housing needs of the MATLAB code.)

说明：  基于完全二次多项式响应面法求结构的失效概率(Failure probability of structure based on response surface method of complete quadratic polynomial)

说明：  11电平的MMC的脉冲触发控制方法，含均压控制。(11 level MMC pulse trigger control method, including voltage sharing control.)

,在MATLAB环境下对 学生自主学习成绩决策表进行了求解,分析了该决策表的上近似集、下近似集、不可分辨关系、约简、核集、属性依赖度的概念,从而 在原始数据的基础上得出了条件属性与决策属性间的关系。 (The knowledge theory based on rough set can help to simplify the knowledge proceeded from originaldata. In the paper it intro- duces the rough set theory and its principle algorithms. MATLAB Rough SetToolbox is programmed to analyze the decision table of self-learn- ing system from students score database.The concepts such as the upper approximation, lower approximation, indiscernibility relation, reduc- tion, core set, and attribute-importance are analyzed based on the score database table.Thus, the relations between condition attributes and de- cision attributes of the score database are obtained on the basis of original data. )

Distributed Beamforming for Relay Networks Based on Second-Order Statistics of the Channel Statement Information最小发送功率与最大信噪比的代码，结果与论文中相同，总共9个仿真图(Networks Based on Second-Order Statistics of the Channel Statement Information。Minimum transmit power with maximum signal-to-noise ratio of the code, the same results paper, a total of nine simulation Figure)